What impact does bias have in Al training data?

What impact does bias have in Al training data?
A . It ensures faster processing of data by the model.
B . It can lead to unfair or incorrect outcomes.
C . It simplifies the algorithm’s complexity.
D . It enhances the model’s performance uniformly across tasks.

View Answer

Answer: B

Explanation:

Definition of Bias: Bias in AI refers to systematic errors that can occur in the model due to prejudiced assumptions made during the data collection, model training, or deployment stages.

Reference: "Bias in AI systems can result from biased data or biased algorithmic processes." (AI Now Institute, 2018)

Impact on Outcomes: Bias can cause AI systems to produce unfair, discriminatory, or incorrect results, which can have serious ethical and legal implications. For example, biased AI in hiring systems can disadvantage certain demographic groups.

Reference: "Bias in AI systems can perpetuate and even amplify existing societal biases." (National Institute of Standards and Technology, 2020)

Mitigation Strategies: Efforts to mitigate bias include diversifying training data, implementing fairness-aware algorithms, and conducting regular audits of AI systems.

Reference: "Addressing AI bias requires comprehensive strategies including diverse data and fairness audits." (Ethics in AI, Oxford University, 2021)